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S O N D J F M A M J J A
2000- 2001 1999- 2000
INSIGHT
Michael F. Easley, Governor Employment Security Commission of North Carolina Thomas Whitaker, Acting Chairman
Volume 2, Number 1 October 2001
North Carolina’s Labor and Economic Outlook
NC Quick Stats: August 2001
Labor Force 3,997,600
Employment 3,796,800
Unemployment 200,800
Unemployment Rate 5.0%
Note: Data are preliminary and are
seasonally adjusted.
INSIDE
Labor Market Abstract ............... 1
Economic Indicators in
North Carolina .............................. 1
Issues in North Carolina’s
Unemployment Insurance System:
Average Duration ......................... 3
Introduction .................................. 3
Economic and Demographic
Determinants of Duration ......... 4
Differences in UI Laws ................ 7
Comparison of North Carolina’s
and Georgia’s Reemployment
Programs ..................................... 7
Conclusion .................................... 9
The Reemployment Outcomes of
Dislocated Manufacturing
Workers ......................................... 11
Methodology: Tracking
Laid- Off Workers ..................... 11
Reemployment After a Layoff .. 13
Wages Earned After a Layoff ... 14
Differences by Age Group ........ 15
Differences by Educational
Level .......................................... 16
Differences by Industry ............ 16
Differences by Race
and Gender................................ 17
Conclusion .................................. 17
( Continued on Page 2)
Labor Market Abstract
During August 2001, the North Carolina seasonally adjusted unemployment
rate decreased to 5.0 percent from 5.3 percent the previous month. During
the same period, the civilian labor force grew by approximately 9,000. Em-ployment
in the service producing industries rose during the month with most
increases occurring in retail trade, hotels & lodging and health services. A
decrease occurred in the manufacturing industry with losses primarily in tex-tiles,
furniture and electronic equipment. Overall, the unemployment level
decreased from an estimated level of 210,800 in July 2001 to 200,800 in Au-gust
2001.
Economic Indicators in North Carolina
Economic indicators used to predict future economic activity are referred to
as leading indicators, while coincident indicators are used to help determine
changes in the economy that are concurrent with such indicators. All graphs
reflect the most recent monthly statewide data.
Insured Unemployment Rates*
Adjusted Unemployment Rates*
Total Nonagricultural Employment, in Thousands*
* Source: ESC, Labor Market Information Division
LMI Happenings ........................ 18
1.0%
2.0%
3.0%
4.0%
5.0%
6.0%
S O N D J F M A M J J A
3,700
3,800
3,900
4,000
4,100
S O N D J F M A M J J A
2
2000- 2001 1999- 2000
Sales and Use Tax Revenues, in Millions
Average Weekly Hours Worked in Manufacturing
Initial Claims
Economic Indicators in North Carolina ( Continued from Page 1)
Percent
Latest Previous Previous Change From
Month Month Year Last Year
Asheville 851 1,529 946 - 10.0
Charlotte 4,027 3,394 2,103 91.5
Durham 1,074 1,126 557 92.8
Fayetteville 1,347 1,661 1,007 33.8
Goldsboro 609 1,144 611 0.0
Greensboro 2,450 2,923 1,834 33.6
Greenville 1,274 1,781 794 60.5
Hickory/ Newton 5,683 10,669 2,099 170.7
Jacksonville 438 365 471 - 7.0
Raleigh 2,834 3,005 1,814 56.2
Wilmington 1,070 1,200 828 29.2
Winston- Salem 2,771 4,837 2,345 18.2
Source: Employment Security Commission
Percent
Latest Previous Previous Change From
Month Month Year Last Year
Asheville 39.8 39.9 41.7 - 4.6
Charlotte/ Gastonia 40.3 39.8 41.7 - 3.4
Greensboro/
Winston- Salem/
High Point 38.9 37.9 40.1 - 3.0
Raleigh/ Durham/
Chapel Hill 41.3 40.3 43.1 - 4.6
Source: Employment Security Commission
Percent
Latest Previous Previous Change From
Month Month Year Last Year
Asheville 211.9 191.3 207.0 2.4
Charlotte 1,024.5 1,124.5 1,062.2 - 3.5
Durham 252.1 253.1 258.4 - 2.4
Fayetteville 197.1 196.9 226.0 - 12.8
Greensboro 521.2 503.4 525.6 -. 01
Greenville 141.9 134.3 143.1 -. 01
Hickory 133.2 125.9 124.1 7.3
Raleigh 617.7 636.0 615.2 0.0
Wilmington 240.4 222.4 224.4 7.1
Winston- Salem 359.6 342.4 369.1 - 2.6
Source: N. C. Department of Revenue, Tax Research Division
Statewide In Selected Cities
In Selected Metropolitan Statistical Areas Statewide
Statewide, in Thousands By ESC Local Offices
( Continued on Page 19)
0
20
40
60
80
100
120
140
160
S O N D J F M A M J J A
37
38
39
40
41
42
43
S O N D J F M A M J J A
$ 0
$ 100
$ 200
$ 300
$ 400
S O N D J F M A M J J A
3
Why is North Carolina’s average du-ration
higher than Georgia’s? This
study looks at this issue from three
perspectives. First of all, there are
economic and demographic differ-ences
in the workforces between the
states. Factors such as the number
of manufacturing workers or the
overall unemployment rate may cause
a given state’s duration to differ from
another state’s, all else equal. The
next section, “ Economic and Demo-graphic
Determinants of Duration,”
attempts to show that some of these
differences imply that North
Carolina’s duration should be higher
than Georgia’s.
Issues in North Carolina’s Unemployment Insurance
System: Average Duration
Introduction
In the Unemployment Insurance ( UI) system, duration refers to the number
of weeks UI claimants receive benefits before returning to work. One of the
primary objectives of the Employment Security Commission ( ESC) is to as-sist
in job search, thereby reducing the average duration of filing for benefits.
A low average duration means UI recipients are returning to work quickly,
thus saving the UI system, and the employers in the state that fund it, consid-erable
sums of money. For example, if North Carolina’s average duration
had been reduced by one week over the last year, the UI Trust Fund would
have saved over $ 64 million. This value is obtained by multiplying the aver-age
weekly benefit amount in the state over the last year ($ 235.31) by the
number of “ first payments” in the same period ( 272,597).
North Carolina would benefit more from a reduction in average duration than
other states in the Southeast because both its average weekly benefit amount
and number of first payments are relatively high. For instance, Georgia and
Virginia, two comparably- sized states, would have saved only $ 44 million and
$ 22 million, respectively, if their durations had been reduced by one week.
Georgia’s average weekly benefit amount was $ 215.39 and its number of
first payments was 203,959 while the corresponding numbers for Virginia
were $ 210.22 and 106,018.
Fortunately, over the last few years North Carolina has had either the 2nd or
3rd lowest duration among the states, after Georgia ( and recently New Hamp-shire).
In the first quarter of 2001, North Carolina’s average duration for the
12- month period was 9.2 weeks, compared to 8.5 weeks for Georgia and 8.7
weeks for New Hampshire. As Figure 1 shows, the average duration in
North Carolina has been consistently above Georgia’s in the last few years. 1
A one- week reduction in North
Carolina’s average duration last
year would have saved its UI Trust
Fund over $ 64 million.
At 9.2 weeks, North Carolina’s
average duration is the third
lowest in the nation.
North Carolina’s low average
duration of filing for UI means
claimants are returning to work
faster than in other states.
7.0
7.5
8.0
8.5
9.0
9.5
10.0
10.5
1998.2 1998.3 1998.4 1999.1 1999.2 1999.3 1999.4 2000.1 2000.2 2000.3 2000.4 2001.1
Quarter
Weeks of Duration
Georgia North Carolina
Figure 1: Average Durations in North Carolina and Georgia
4
Secondly, there are differences in UI laws between North Carolina and Geor-gia.
Although the states must conform to general federal guidelines when
operating a UI system, each state has some flexibility in the procedures of its
UI system. For example, states have different maximum weekly benefit
amounts and they may establish different laws for allowing UI claimants to
refuse job offers. These differences, detailed in the section “ Differences in
UI Laws,” can have important implications for the length of average dura-tions.
Thirdly, reemployment programs in the respective states are likely to have an
important impact on duration. North Carolina and Georgia have both imple-mented
special reemployment initiatives in the last few years. We will
compare these initiatives in the section titled “ Comparisons of North Carolina’s
and Georgia’s Reemployment Programs.”
Economic and Demographic Determinants of Duration
In order to compare North
Carolina’s duration to other
states’, one should look at how
the characteristics of the
economy and workers in the
state affect average duration.
Using just some of the charac-teristics
of the workforce and
other economic factors that are
important to duration, we have
predicted the average duration
for the 50 states. Figure 2 shows
the predicted durations and the
actual durations for the seven
southeastern states. ( See Ap-pendix
1 for a graph of all 50
states.)
North Carolina’s predicted du-ration
is higher than several other southern states, particularly Alabama, South
Carolina, Georgia, Tennessee and Virginia. North Carolina’s actual average
duration was 2.9 weeks shorter than predicted by this model, which was the
largest difference of the states in the region.
The predicted durations were derived using a regression analysis on data
from the 50 states in 2000. With this regression, one can make some general
statements about how certain variables affect duration and how, when taken
together, these variables impact North Carolina’s duration relative to other
states.
Six variables were used to predict average duration. These variables were
chosen based on assumptions that they were important factors in determin-ing
duration, as explained in the following paragraphs. In addition, data on
these variables were readily available for all 50 states.
Differences in UI laws and
reemployment programs in the
respective states may explain the
differences in duration.
Regression analysis on data from
50 states in 2000 predicted North
Carolina’s duration to be higher
than several other southern states,
particularly Alabama, South
Carolina, Georgia, Tennessee and
Virginia.
0
2
4
6
8
10
12
14
16
AL SC GA TN VA NC FL
Weeks
Average Duration Predicted Duration
Figure 2: Average Duration in 2000 Compared with Predicted Duration
5
The first of these variables is the num-ber
of workers in the state who were
covered by unemployment insurance,
i. e. covered employment, in 2000. It
is expected that states with larger cov-ered
employment will have a higher
average duration. Employment of-fices
in larger states may often face
a greater number of job applicants.
North Carolina’s covered employment
is among the largest in the Southeast
region, as shown in Figure 3. How-ever,
it is less than Florida’s and not
significantly higher than either
Georgia’s or Virginia’s.
The second determinant of duration
used is the share of manufacturing
employment in the overall covered em-ployment
in the state. It is expected
that states with more manufacturing
will have higher durations, since many
manufacturing workers have more dif-ficulty
finding reemployment than
workers in other industries. In sup-port
of this, Current Population Survey
( CPS) data show that unemployed
manufacturing workers have longer in-dividual
durations than workers from
other industries. As Figure 4 shows,
among the Southeastern states, North
Carolina has the highest percentage
of its covered workers in the manu-facturing
sector.
The third variable is the maximum
weekly benefit amount for UI recipi-ents
in 2000. It is expected that a
higher maximum benefit amount will
increase duration. More generous
benefits may delay the need for
workers to find reemployment. Fig-ure
5 shows that, among the states in
the Southeast, North Carolina had the
highest maximum weekly benefit
amount as of July 2001. This rose to
$ 396 August 1, 2001.
Figure 3: Covered Employment in 2000
3,823 3,773 3,765
3,230
2,605
1,823 1,784
0
1,000
2,000
3,000
4,000
5,000
FL NC GA VA TN AL SC
In Thousands
Figure 4: Share of Manufacturing
Employment in Overall Employment in 1999
21.6%
20.4% 20.0% 19.7%
16.2%
12.6%
7.4%
0.0%
5.0%
10.0%
15.0%
20.0%
25.0%
NC AL TN SC GA VA FL
$ 331
$ 275 $ 274 $ 259 $ 255
$ 190
$ 375
$ 0
$ 50
$ 100
$ 150
$ 200
$ 250
$ 300
$ 350
$ 400
NC VA FL GA SC TN AL
Dollars per week
Figure 5: Maximum Weekly Benefit Amounts in 2000
6
The average unemployment rate in the
state during fourth quarter 2000 is the
fourth variable. A higher unemployment
rate would imply a weaker labor market,
so that unemployed workers face a
harder time finding employment. This
would lengthen duration in states with high
unemployment. As Figure 6 shows,
North Carolina’s unemployment rate was
high relative to most of the other states
in the region during fourth quarter 2000.
The fifth factor considered important to
duration is whether the state has a one-week
waiting period for UI recipients.
Only twelve states do not have waiting
periods. Two of these states, Georgia
and Alabama, are in the Southeast. These
states may have a higher proportion of short- term unemployed filing for ben-efits
in that first week, which would reduce the state’s average duration.
The final determinant of duration is whether a state is located in the South.
This is used to isolate the idiosyncratic nature of the Southern labor market.
For instance, the lower unionization of the labor force in the South may in-crease
job availability and turnover. Given this, it is likely that duration will be
shorter in Southern states. All of the states in our region are expected to
have lower durations because of this.
The results of the regression are summarized in the following table. All of
the variables had the expected impact on duration, except the share of manu-facturing
in covered employment. The lower duration for states with higher
shares of manufacturing employment may be due to the high proportion of
attached claimants within the manufacturing sector. Attached claimants spend
a short time receiving benefits before returning to work with their company.
North Carolina has a high proportion of attached claims. The impact of
attached claimants cannot be directly obtained because the data on the other
states are unavailable.
Variables affecting duration . . . . . . and their impact estimated by the model
1. covered employment an additional one million workers raised duration by about
two- tenths of a week
2. share of manufacturing in employment a 10 percentage point increase ( from, say, 20% to 30%)
lowered duration by a little more than one week
3. maximum benefit amount a $ 10 increase raised duration by one- tenth of a week
4. average total unemployment rate a 1 percentage point increase raised duration by about
two- thirds of a week
5. 1- week waiting period increased duration by approximately .8 weeks
6. Southern state reduced duration by 1.2 weeks
A one- week waiting period for UI
recipients may affect a state’s
duration rate.
The proportion of attached claims
filed within the manufacturing
sector affects duration.
4.4%
3.8% 3.6% 3.5%
3.2%
2.9%
2.1%
0.0%
1.0%
2.0%
3.0%
4.0%
5.0%
AL TN NC FL GA SC VA
Figure 6: Fourth Quarter 2000 Average Total Unemployment Rates
7
Although the regression explained over half the differences in the actual
durations of the 50 states, it did not predict every state’s duration exactly.
For instance, it overestimated North Carolina’s average duration by nearly
three weeks. There are other factors affecting duration that were not con-sidered
in the model. As mentioned earlier, the number of attached claimants
would be important. A second factor is the reemployment program in the
state, discussed later. Still other factors include the age and racial distribu-tion
of the state’s workforce, as well as, the amount of urbanization within
the state.
Differences in UI Laws
North Carolina and Georgia differ in eligibility requirements and benefits, as
established by their respective UI laws. As previously stated, North Caro-lina
has a higher maximum weekly benefit amount than Georgia, which
contributes to a higher expected duration. North Carolina’s maximum weekly
benefit was $ 375, compared to $ 274 in Georgia. This difference is a result of
the way the two states calculate the maximum weekly benefit: in North
Carolina, it is two- thirds of the average weekly wage in the state while in
Georgia it is less than one- half. Therefore, UI recipients whose high- quarter
earnings are relatively high will receive a larger proportion of their pre- layoff
wages in benefits in North Carolina than in Georgia. For example, a worker
with average weekly earnings of $ 800 would only receive 34% of this in UI
benefits in Georgia, but would have a wage replacement rate of 47% in
North Carolina. Thus, claimants in Georgia have a greater incentive to find
new jobs quickly.
Under the different state laws, it seems that claimants have an easier time
rejecting job offers in North Carolina than in Georgia. Georgia’s law speci-fies
that individuals who receive benefits for 10 or more weeks cannot reject
a job offer if the wages are at least 66 percent of their high- quarter base
period wages. North Carolina does not have such a provision. However, it
is a general practice in North Carolina’s local ESC offices to encourage
claimants who have been unemployed for many weeks to accept jobs which
offer lower wages. Also, North Carolina has a provision in its law that al-lows
individuals to refuse a job if they cannot obtain adequate childcare or
elder care.
Both North Carolina and Georgia determine the duration of benefits based
on wages earned in the base period. Most UI recipients in both states will be
eligible for 26 weeks of benefits. But if workers earned relatively little in the
entire base period compared to the high quarter, then the benefit period may
be reduced. In North Carolina, the minimum benefit period is 13 weeks,
while in Georgia the benefit period may be as low as eight weeks.
Comparison of North Carolina’s and Georgia’s Reemployment
Programs
Again, both North Carolina and Georgia have received state funds in order to
provide a reemployment program for eligible claimants that are receiving
unemployment benefits. The North Carolina program, the Reemployment
Initiative ( REI), was funded in January 2000 and was implemented in April
All factors which may affect
duration were not considered in
the model.
The difference in the way the two
states calculate the maximum
weekly benefit creates a
significant difference in what each
state pays.
There are also differences in
claimant eligibility requirements.
Maximum eligibility in both states
is 26 weeks of benefits.
8
2000. Georgia’s program, the Claimant Assistance Program ( CAP), began
with service to select areas of Georgia in 1987, but expanded to cover the
entire state eighteen months later. Georgia’s CAP was used by North Caro-lina
as a model for its REI; therefore, there are many similarities between the
programs. Some differences exist, as well.
The CAP went through a slow and evolving process over the years to be-come
what it is today. Initially, CAP provided one- on- one contact with
claimants and also offered workshops. Now, almost all efforts are in the
form of one- hour specialty workshops designed to meet the needs of the
claimants based on their input and suggestions. There is, however, one- on-one
time still available for claimants at the workshops. Until recently, CAP
participants included only claimants that were separated from work through
lack of work. Now the program includes those who are without work for
other reasons, as well, such as quitting due to child or elder care or other
cause, being fired, etc. North Carolina’s REI only includes those that have
been separated through lack of work.
The CAP local office staff undergoes training involving six consecutive courses
taught by consultants. Staff members of each district meet every six months
to discuss the program and ways they can improve their performance. There
is also an emphasis on trying to ensure that the most successful staff are
used in the program. Staff members participate in an information exchange
program that matches low performance workers with high performance ones
to improve overall quality. There is consistent monitoring of the performance
of staff members.
One of the reasons Georgia is able to provide so much training for its staff is
that it receives more appropriated funds for its program. Georgia has re-ceived
between $ 14- 19 million per year for the implementation of CAP. This
compares to approximately $ 9 million that was appropriated for the REI pro-gram
in North Carolina during its first year of implementation. CAP is funded
for a five- year time period while REI is funded for two years at a time. Also,
Georgia has received no indication from its legislature that it plans to termi-nate
CAP, while REI funding is not included in North Carolina’s budget
effective July 1, 2001.
Georgia and North Carolina both employ 160-
200 staff members in their programs. However,
Georgia has 53 offices statewide while North
Carolina has around 90 offices. This results in
more staff available at each office in Georgia.
The REI program served approximately 67,000
claimants last year, while CAP served 58,000.
However, the programs offered by the CAP
staff were also provided to 9,000 participants
in Georgia’s UI profiling program.
The CAP is a 17- week long program while REI
is 12 weeks long. Participants in CAP are re-quired
to meet with a staff person after the
first, fifth, ninth and fourteenth weeks of the
Georgia’s reemployment initiative
program was used by North
Carolina as a model for its REI.
In CAP, both methods of providing
claimants services and staff
training have evolved extensively
over time.
Georgia has received between
$ 14- 19 million yearly for CAP
while North Carolina received $ 9
million in the first year for REI.
67,000
58,000
0
10,000
20,000
30,000
40,000
50,000
60,000
70,000
80,000
North Carolina Georgia
Number of Claimants Served by a Reemployment Program in 2000
9
program. Contact is not required after the seventeenth week. REI participants are required to contact staff
either in person, by phone or by e- mail on a weekly basis for the first four weeks and biweekly for the remaining
eight weeks. CAP directs its participants into one of three tracks: self- serve, staff assisted and intensive. In both
programs, participants are subject to adjudication if they do not follow the expectations of the program. However,
this does not happen often.
For the year ending June 2001, Georgia received approximately 252,000 initial separated claims while North
Carolina received around 295,000. Of these, about 23 percent in each state participated in their respective
reemployment program, CAP or REI. These efforts resulted in an entered employment rate of 52.7 percent for
the 17- week CAP and a 44.4 percent rate for the 12- week REI, which in both cases amounts to roughly 30,000
people.
While the main goal of both CAP and REI
is to aid in UI claimants reentering the
workforce as soon as possible, another ben-efit
of both programs is to increase the
savings to each state’s UI Trust Fund. One
way this is obtained is by lowering the du-ration.
Although Georgia’s overall duration
is lower than North Carolina’s, the savings
are somewhat different. It is estimated that
CAP saved Georgia’s trust fund $ 38.9 mil-lion
for one year and North Carolina saved
its trust fund an estimated $ 42 million for
the same time period. Given that roughly
$ 5- 10 million more is spent on Georgia’s
CAP compared to REI, North Carolina’s
REI program is more cost effective.
Both the CAP and REI are beneficial to their participants and trust funds in their respective states. One might
argue that Georgia has been more successful in its reemployment efforts because it has been operating this
program for over a decade and funds it at a higher level than North Carolina. However, because these programs
are so similar and because they affect only a portion of UI claimants in each state, it is unlikely that the differ-ences
between the programs contribute much to the differences between average durations in these two states.
Conclusion
Shortening the length of time UI claimants receive benefits, or average duration, provides significant savings to a
state’s UI system. Currently, North Carolina has the third- lowest average duration among the states, although
Georgia’s duration is .7 weeks shorter. This article has attempted to cover some of the factors that explain why
one state’s duration is not as low as another’s.
Based on the regression analysis of data from the 50 states, North Carolina is expected to have a higher duration
of filing for UI benefits than many of the other states in our geographic area, including Georgia. One of the
factors which tends to push up North Carolina’s duration is its high maximum weekly benefit amount. North
Carolina has the highest maximum weekly benefit of all the states in the Southeast; it is approximately $ 100 more
than Georgia’s. The one- week waiting period for UI recipients is another factor increasing North Carolina’s
duration relative to Georgia’s. Also, North Carolina’s relatively high unemployment rate should make it harder for
UI claimants to find reemployment quickly.
In addition to the variables used in the regression, differences in UI laws between North Carolina and Georgia
would likely imply a higher duration in North Carolina. For instance, North Carolina’s laws permit claimants to
reject a wider range of unfavorable job offers.
$ 39
$ 42
$ 0
$ 5
$ 10
$ 15
$ 20
$ 25
$ 30
$ 35
$ 40
$ 45
North Carolina Georgia
Dollars in Millions
Trust Fund Savings from Reemployment Programs in 2000
10
However, North Carolina’s Employment Service ( ES), particularly its Reem-ployment
Initiative staff, has been very successful in job placement and
employment services. Over the past year, over 770,000 individuals regis-tered
for employment services and 213,005 entered employment after receiving
ES services. During the same period, intensive reemployment assistance
was offered to approximately 67,000 claimants. For these efforts, the aver-age
length of weeks claimants file for UI before obtaining work is currently
9.2 weeks, the third lowest in the nation.
North Carolina’s ESC administrative staff, as well as ES and REI staff, have
one goal in mind: assisting unemployed workers find employment quickly.
An important result of these efforts is a low duration of filing, which provides
savings to the UI Trust Fund and the state’s employers.
1 Average duration is calculated by dividing the weeks compensated in the previous
12 months by the number of first payments in the same period.
North Carolina has the third
lowest average duration for UI
claimants in the nation at 9.2
weeks.
0
2
4
6
8
10
12
14
16
18
AL SC NH MS GA VT AR WI IA SD TN IN VA MI NE DE KY NC MO CT OK ND AZ KS MD
Average Duration Predicted Duration
Appendix 1: Average Duration in 2000 Compared with Predicted Duration for All States
0
2
4
6
8
10
12
14
16
18
ME UT LA ID CO MN FL NV WY TX OR OH RI MT WV NM NJ HI IL PA WA AK CA MA NY
Average Duration Predicted Duration
11
The Reemployment Outcomes of Dislocated
Manufacturing Workers
Recent adverse economic conditions have par-ticularly
impacted the manufacturing sector in
North Carolina. The events of September 11th
will likely contribute to this, as the decline in the
airline and tourist- related industries further re-duces
demand for manufactured products.
Although most of the recent layoffs in manufac-turing
seem to be temporary, there have been
several permanent layoff events in the state in
the last few months. In addition, the volatility in
manufacturing employment may encourage some
workers to voluntarily leave this industry group
in search of a more stable career.
As in most states, the share of manufacturing
employment has been declining in North Caro-lina
for decades. This has led to an absolute drop
in employment in this industry over the last few
years. Between August 1995 and August of this year, employment in manu-facturing
has steadily declined from 862,500 to 731,900. The majority of this
decline has been in the textile industry, where employment has fallen by
approximately 67,000 over this time period. Total employment in the state,
however, continued to rise, with most of the new jobs being reported in the
services and retail trade sectors. Industrial employment projections suggest
that this trend will continue for the near future.
Do dislocated manufacturing workers find jobs with comparable wages?
Wages in the manufacturing sector are somewhat above the state average.
In 1998, the average weekly wage in manufacturing, according to the Em-ployment
and Wages program of the Employment Security Commission ( ESC),
was $ 629.84, 17 percent above the state average wage across all industries.
At the same time, the services industry’s average wage was $ 504.88. Of
course, this is a crude comparison because it does not take into account the
experience or education of the individual workers who make up the employ-ment
in these industries. Such data, as well as anecdotal evidence, do not
give a satisfactorily in- depth answer to how well the dislocated workers do
at maintaining their standards of living.
In this article, we take a different approach by following workers who were
dislocated from their jobs in 1997 and 1998 in order to see how their indi-vidual
wages two years after the layoffs compare to their pre- layoff wages.
This provides a view of a large number of workers during that period, to see
where they actually found a job and how much they actually earned. This
information is pertinent to dislocated workers today.
Methodology: Tracking Laid- Off Workers
The North Carolina ESC has developed a data series which tracks workers
who have lost their jobs due to business closures or permanent layoffs. These
workers, and the companies which laid them off, are identified by the state’s
Manufacturing employment in
North Carolina has been
declining for decades.
In 1998, the average weekly wage
in manufacturing was $ 629.84,
17 percent above the state
average wage across all
industries.
640
680
720
760
800
840
880
1995 1996 1997 1998 1999 2000 2001
In Thousands
Figure 1: Manufacturing Employment in North Carolina
August 1995 - August 2001
12
Mass Layoff Statistics ( MLS) program. This program only identifies busi-nesses
with at least 50 claimants for Unemployment Insurance ( UI) in a
consecutive 5- week period. The data show the employment history and
wage earnings of these workers for four quarters before their separation and
eight quarters afterwards.
The data series answers many questions about laid- off workers, among them:
· In what industries will the laid- off workers find new jobs?
· How much will the laid- off workers earn in their new jobs compared
with their old jobs?
· How long will it take for the laid- off workers to find new jobs?
UI taxes are collected by ESC on a quarterly basis, with no reference to a
person’s starting date within the quarter. Therefore, only quarterly wage
data were given in this series. This presents some limitations because the
hours a given person works during a quarter are not known. For example, if
a worker’s wages are lower in one quarter than another, we cannot deter-mine
if this is caused by a lower hourly wage as opposed to fewer hours of
work. Such information could be obtained by a survey of dislocated work-ers,
but this would be costly and time- consuming.
To calculate the pre- layoff quarterly wage, only the wage data on the four
quarters before the layoff event occurred were used. The maximum quar-terly
wages of the first three quarters of the year were used in this study.
( The 4th quarter [ Oct. – Dec.] was excluded because wages are typically
higher in this quarter due to seasonal reasons, such as end- of- year bonuses
and holiday- related employment. Although earnings in the fourth quarter
should be considered when estimating a worker’s yearly income, we are
only interested in quarterly earnings.)
Post- layoff wages were limited to employers covered by UI in North Caro-lina.
Data could not be tabulated for workers who are self- employed or who
earned wages in a state other than North Carolina.
Table 1 shows the total number of layoffs and manufacturing layoffs for
each quarter during the time period under study — first three quarters of
1997 and 1998. These two years were chosen because they were the most
recent years for which complete data were available. They are somewhat
atypical because the unemployment rate in North Carolina during this period,
and the following two years, was consistently below 4 percent. The majority
of layoffs occurred in the 1st and 2nd quarters of each year.
Table 1: Mass Layoffs by Quarter
Year. Quarter Total Layoffs Manufacturing Layoffs
1997.1 3,077 2,588
1997.2 2,860 1,833
1997.3 1,590 1,436
1998.1 4,158 2,930
1998.2 3,554 2,974
1998.3 1,447 1,110
Lower wages in one quarter may
be a result of fewer hours of work.
Workers involved in this study
were followed four quarters before
layoff and eight quarters after
layoff.
Pre- and post- layoff wage
information were limited to
covered wages in North Carolina.
13
Of the 16,686 workers laid off, 12,871 ( 77 percent) were employed by com-panies
in the manufacturing sector. In some cases, individuals were laid off
more than once during this period. When this occurred, the later observa-tions
for those individuals were deleted. Also, workers who were laid off by
a company in the “ tobacco products” industry were excluded because these
companies tend to have seasonal layoffs. This left 9,405 individuals in the
database. Four industries were responsible for over three- quarters of these
layoffs: textiles, apparel, industrial and commercial machinery, including com-puter
equipment, and furniture.
Reemployment After a Layoff
Approximately 67 percent of the laid- off manufacturing workers found re-employment
within one year and 74 percent were reemployed at the end of
two years. As previously stated, these percentages did not reflect the num-ber
of workers who became self- employed, found jobs outside the state or
retired.
Where did these workers find jobs? The steady decline in the overall num-ber
of manufacturing jobs in the state limits the possibility of reemployment in
these industries. The opportunities available for workers would be deter-mined
by the overall health of the economy and the availability of jobs in the
local area that pay enough to ensure a decent standard of living.
Table 2 shows the industries in which the laid off manufacturing workers
were employed two years after the lay- off event. Many of these workers
who returned to work were able to find jobs in the manufacturing sector. Of
the 44 percent reemployed within the manufacturing industries, 16 percent
were reemployed by their former employer and another 28 percent were
reemployed by a company within the same industry. As expected, a substan-tial
fraction were reemployed in either the services sector ( 24 percent) or the
Prior to analysis, adjustments
were made to the data to account
for anomilies.
Forty- four percent of those
workers who returned to work
found jobs in the manufacturing
industries.
retail trade sector ( 9 percent).
Table 2: Primary Industries of Reemployed Manufacturing Workers Two Years after Separation*
Industry Divisions Percent
Same industry** 19
Manufacturing ( other than same industry) 25
Services 24
Retail Trade 9
Wholesale Trade 4
Construction 3
Government 2
Transportation, Communications and Public Utilities 2
Finance, Insurance and Real Estate 1
Agriculture 1
Industry Code not available 10
* These numbers do not include quarters 1997.1 and 1997.2 due to a large proportion of missing industry codes.
* * Thirty- seven percent of these were reemployed by the same company that laid them off.
14
The reemployment opportunities in the manufacturing sector allowed many workers to find jobs where the skills
they had learned over many years could be used effectively. In the next section, we see that this helps those
workers who find manufacturing jobs retain their relatively high wages. But what happens to the wages of
workers who are unable to return to a manufacturing job?
Wages Earned After a Layoff
The median pre- layoff quarterly wages of
the workers was $ 4,895. One year after
the layoff event, the median wages of the
workers who were reemployed was $ 3,781,
a 23 percent decrease. By the end of two
years, median quarterly wages had risen to
$ 4,329, which was still 12 percent lower
than the wages of this group before the lay-off.
( The median pre- layoff wages of the
workers who were employed two years
after the layoff were slightly higher than
the median wages of all the workers who
were laid off.)
Figure 2 compares the median wages of
workers two years after the layoff to their
pre- layoff wages, based on the industry in which the workers were reemployed. It is important to note that
reemployment occurred mainly in the manufacturing, services and retail trade sectors. ( Refer to Table 2.)
The median wage of the workers who were reemployed in manufacturing was approximately the same as the
median wage of this group before the layoff. Those reemployed by the same company or in the same sub-industry
tended to do slightly better than those who found employment in a different manufacturing industry.
Those who found jobs in the services and retail trade industries faced significant reductions in their quarterly
wages. The median wage for the workers reemployed in the retail trade industry was only 63 percent of the pre-layoff
median wage for this group. It should be noted that the workers who went into the services or retail trade
sectors had lower pre- layoff median wages than the other workers. This may suggest that they had, on average,
lower skills or less tenure than other workers.
Another way of looking at the change in
wages before the layoff and afterwards
is to look at the distribution of each
individual’s change in wages. This
analysis is summarized in Figure 3. Two
years after the layoff event, 53 percent
of the workers who were working earned
less than 90 percent of their pre- layoff
wages; 18 percent earned within 10 per-cent
of their pre- layoff wages; and 29
percent earned more than 110 percent
of their pre- layoff wages. Furthermore,
20 percent earned less than half their
pre- layoff wages and 2 percent earned
more than twice as much.
87%
88%
87%
94%
$ 0
$ 1,000
$ 2,000
$ 3,000
$ 4,000
$ 5,000
$ 6,000
Less than 9 years 9 - 11 years 12 years 13 - 16 years
Median Pre- Layoff Wages Median Wages Two Years after Layoff
Figure 2: Quarterly Median wages of Reemployed Manufacturing
Workers Two Years After Layoff
53%
18%
29%
Earned Less Than 90%
Earned Within 10%
Earned More Than 110%
Figure 3: Wages Earned Two Years After Layoff as a Percentage of
Pre- Layoff Wages
15
One may assume that workers with higher wages before the layoff did better than lower- income workers.
Presumably, workers with higher incomes have more job skills, gained through a combination of education and
on- the- job training. Many of these skills can be transferred into new careers, allowing the workers to continue
earning relatively high wages. However, some of the skills may not be transferable. The returns to these job-specific
skills could be lost when workers move into other occupations.
As previously stated, workers with
higher quarterly wages before a lay-off
earned higher wages two years
afterwards than workers with lower
pre- layoff wages. However, Figure 4
shows that the higher- paid workers did
not do as well at replacing their wages.
Workers in the highest income group
had the lowest wage replacement rate
( 58 percent).
Also, only 67 percent of the workers
in the highest wage category were re-employed
by a company in the state
two years after the layoff, which is sig-nificantly
lower than the reemployment
rates of the other groups. It is likely
that a larger fraction of these workers
sought employment outside the state or became self- employed. However, as noted earlier, the data do not
contain information on the wages of these workers.
Differences by Age Group
One might expect that workers in different age brackets would have different experiences after a layoff. Young
workers were more likely to do well after being laid off than older workers because they had put less of an
investment into their jobs and had a greater incentive to move to a different job in order to develop new skills.
Older workers had invested a lot of their time developing skills which were specific to their jobs and, therefore,
would likely find it harder to get new jobs outside their field that paid as well.
Figure 5 shows the median pre- layoff
wages of workers compared to their
wages two years after the layoff event,
where workers were separated into age
groups. The workers under the age of
30 earned 90 percent of their pre- layoff
wage, while workers in the 55 and over
age group earned only 78 percent of their
pre- layoff wage. The median wage in
the middle two age groups was approxi-mately
87 percent of the pre- layoff
median wage. Older workers were also
less likely to re- enter the job market.
Only 52 percent of the workers aged 55
and over had returned to work after two
years, compared to approximately 77
percent of the workers in the other age
groups.
58%
73%
86%
93%
117%
$ 0
$ 2,000
$ 4,000
$ 6,000
$ 8,000
$ 10,000
$ 12,000
$ 0-
$ 2,999
$ 3,000 -
$ 4,999
$ 5,000 -
$ 6,999
$ 7,000 -
$ 9,999
$ 10,000 -
$ 14,999
Pre- Layoff Wages
Median Pre- Layoff Wage Median Wage Two Years after Layoff
Figure 4: Quarterly Median Wages of Manufacturing Workers by Pre- Layoff
Wage Categories
90% 87% 87%
78%
$ 0
$ 1,000
$ 2,000
$ 3,000
$ 4,000
$ 5,000
$ 6,000
Under Age 30 Age 30 to 44 Age 45 to 54 Age 55 and Over
Median Pre- Layoff Wage Median Wages Two Years after Layoff
Figure 5: Median Wages Two Years After Layoff Compared to Pre- Layoff
Wages - by Age Group
16
Differences by Educational Level
Education is an important indicator of earnings. More educated workers typically have greater opportunities for
employment and earn higher wages. Figure 6 compares the pre- layoff wages to wages earned two years after
the layoff by educational level. Both before and after the layoff, median wages increased as the level of educa-tion
rose. However, workers in manufacturing who have more years of education are challenged to find jobs
which allow them to sustain their relatively higher standard of living; many of them may possess firm- specific
skills which will not be compen-sated
by a different employer.
The group of workers with 12 years
of education made up 55 percent
of the sample. The median wage
for this group two years after the
layoff was 88 percent of the pre-layoff
wage. The median wage of
the workers with less than nine
years of education was 94 percent
of the pre- layoff median wage for
this group. However, only 58 per-cent
of this group were reemployed
two years after their layoff. This
might suggest that many of the large
number of older workers in this
group withdrew from the labor mar-ket
to retire or return to school for
further training.
Differences by Industry
As stated earlier, there were
four manufacturing industries
which contributed the majority
of the layoffs in our database:
textiles, apparel, industrial and
commercial machinery, includ-ing
computer equipment, and
furniture. These, notably, are
also the industries that seem to
have been particularly hard hit
during the current economic
downturn. Figure 7 shows the
median wages in each of these
industries before a layoff and
two years afterwards for laid-off
workers who found
reemployment in the state.
Median wages were highest in
the textile and industrial machin-ery
industries before the layoff,
but workers in the furniture in-dustry
did relatively better after
the layoff.
87%
88%
87%
94%
$ 0
$ 1,000
$ 2,000
$ 3,000
$ 4,000
$ 5,000
$ 6,000
Less than 9 years 9 - 11 years 12 years 13 - 16 years
Median Pre- Layoff Wages Median Wages Two Years after Layoff
Figure 6: Median Wages Two Years After Layoff Compared to Pre- Layoff
Wages - by Educational Levels
94%
106%
88%
84%
$ 0
$ 1,000
$ 2,000
$ 3,000
$ 4,000
$ 5,000
$ 6,000
Textiles Apparel Furniture Industrial and
Commercial
Machinery
Median Pre- Layoff Wages Median Wages Two Years after Layoff
Figure 7: Median Wages Two Years After Layoff Compared to Pre- Layoff
Wages - by Industry
17
Workers in the furniture industry were much more likely to find reemployment with other companies within the
same industry than workers in the other industries. This may be due to the geographic concentration of the
companies that manufacture furniture. Workers in the industrial machinery industry were most likely to be
reemployed in the services sector of the economy. However, most of these service jobs were in the “ help supply
services,” which includes many high- tech contract workers.
Differences by Race and Gender
Figure 8 shows the median wages
of the workers who were laid off
by gender. There are significant dif-ferences
between the median wages
of men and women before the lay-offs.
Much of this difference was
attributable to the fact that a larger
percentage of the women were em-ployed
in the low- paying apparel
industry ( 20 percent, compared to 8
percent of men). After two years,
the median wage of the women was
85 percent of the median pre- layoff
wage, while the comparable figure
for men was 89 percent. The re-employment
rates between men and
women were approximately equal.
As illustrated in Figure 9, there was not a significant difference in the median pre- layoff wage of workers among
the racial groups. The median wage for whites two years after the layoff was 86 percent of the pre- layoff
median wage for this group, compared to 88 percent for blacks and 92 percent for the other racial groups
combined ( Native Americans, Asians and Hispanics). The only significant difference was in the reemployment
rates: approximately 78 percent of blacks had returned to a job within the state, compared to 71 percent of
whites.
Conclusion
Recent dislocations in the manufac-turing
sector of the North Carolina
economy have led to great concern
about the future living standards of
those workers who have been laid
off. This study has looked at manu-facturing
workers who were laid off
in either 1997 or 1998, and who filed
for UI benefits, to identify their re-employment
experiences related to
where they achieved reemployment
and how much they earned as a per-centage
of their earnings before the
layoff ( that is, their wage replace-ment
rates).
85%
89%
$ 0
$ 1,000
$ 2,000
$ 3,000
$ 4,000
$ 5,000
$ 6,000
Men Women
Median Pre- Layoff Wage Median Wage Two Years after Layoff
Figure 8: A Comparison of Median Pre- Layoff Wage to Median Wage Two Years
After Layoff - by Gender
92%
88%
86%
$ 0
$ 1,000
$ 2,000
$ 3,000
$ 4,000
$ 5,000
$ 6,000
White Black Other
Median Pre- Layoff Wages Median Wages Two Years after Layoff
Figure 9: Median Wages Two Years After Layoff Compared to Pre- Layoff
Wages - by Race
18
Overall, 74 percent of the dislocated manufacturing workers were reem-ployed
in the state after two years. The median wage of these workers was
88 percent of the median pre- layoff wage. The workers who were reem-ployed
in the manufacturing sector were able to retain their pre- layoff
standards of living, based on the median wages of this group. The workers
who fared the worst were reemployed in either the services or retail trade
industries. Unfortunately, a large fraction of workers ( at least one- third) fell
into these categories.
This study also compared the wage replacement rates of workers in differ-ent
demographic groups. Among the findings: ( 1) lower- paid workers had
higher replacement rates than high- paid workers, ( 2) workers under the age
of 30 did better than older workers, ( 3) workers with less than nine years of
education had the highest replacement rate, ( 4) men did slightly better than
women, and ( 5) whites did slightly less well than blacks or other races. These
results are based on median wages; individual outcomes may vary.
The results of this study indicate that in an economy where workers are
expected to move from the manufacturing sector to service- related indus-tries,
more must be done to ensure that the workers’ standards of living are
maintained. Among the measures that might be appropriate are skill training
in service industry occupations, educational advancements and specialized
job placement services.
Those reemployed within the
manufacturing sector fared better
than those reemployed within the
services or retail trade industries.
In moving from manufacturing to
service industry employment,
workers may need specialized
assistance.
Internet Usage in Filing Claims
Offering those laid off from their jobs the ability to file initial
claims via the Internet is an example of ESC's commitment to
the convenience of its customers. Internet filing of initial claims
was first offered to claimants in October 2000. During that pe-riod,
43 initial claims were submitted. This has grown to 2,512 in
August 2001.
LMI Happenings: New Research and Products from the LMI Division
Changes in Occupational Employment
Statistics
In an effort to provide more timely occupational
employment by industry and occupational wage
information, the Occupational Employment
Statistical ( OES) survey will shift from being
annual to biannual starting with the 2001
survey. North Carolina is one of five states
that volunteered to test the two panel concept.
Those normally surveyed annually, 13,000
employers representing 1.1 million employed,
will be divided equally between the two panels.
Each panel will still include the basic
components and requirements of a yearly
survey. The Bureau of Labor Statistics, who
draws North Carolina’s sample and oversees
its management, will continue to receive
monthly reports as to the progress. The North
Carolina OES staff’s mission as a volunteer
state is to document, particularly logistically,
any problems and to troubleshoot any
unforeseen occurrences. Being a test state
enables North Carolina to “ wet its feet” before
the rest of the nation undertakes the new two
panel format.
0
500
1,000
1,500
2,000
2,500
3,000
Oct.
2000
Nov.
2000
Dec.
2000
Jan.
2001
Feb.
2001
Mar.
2001
Apr.
2001
May
2001
Jun.
2001
Jul.
2001
Aug.
2001
Submitted Internet Initial Claims
19
2000- 2001 1999- 2000
New Vehicle Registrations Housing Units Authorized by Building Permits
Unadjusted Construction Employment, in Thousands New Business Incorporations
Personal Income Tax Revenues, in Millions
Economic Indicators in North Carolina ( Continued from Page 2)
Total Tax Revenues, in Millions
Source: US Census Bureau
Source: NC Department of Revenue
Source: NC Secretary of State, Corporations Division
Source: NC Automobile Dealers Association
Source: ESC, Labor Market Information Division
Source: NC Department of Revenue
0
10,000
20,000
30,000
40,000
50,000
60,000
S O N D J F M A M J J A
0
2,000
4,000
6,000
8,000
10,000
S O N D J F M A M J J A
200
210
220
230
240
250
260
S O N D J F M A M J J A
0
1,000
2,000
3,000
4,000
5,000
6,000
S O N D J F M A M J J A
$ 0
$ 500
$ 1,000
$ 1,500
$ 2,000
S O N D J F M A M J J A
$ 0
$ 200
$ 400
$ 600
$ 800
$ 1,000
$ 1,200
$ 1,400
S O N D J F M A M J J A
20
750 copies of this publication were
produced at a cost of $ 225.00 or
$ 0.30 per copy.
EMPLOYMENT SECURITY COMMISSION OF NORTH CAROLINA
www. ncesc. com
NCESC 6059 October 2001
LABOR MARKET INFORMATION DIVISION
EMPLOYMENT SECURITY COMMISSION OF NORTH CAROLINA
POST OFFICE BOX 25903
RALEIGH, NORTH CAROLINA 27611- 5903
OFFICIAL BUSINESS
PENALTY FOR PRIVATE USE $ 300.00
FIRST CLASS MAIL
POSTAGE AND FEES PAID
U. S. DEPARTMENT OF LABOR
PERMIT NO. G- 12

1
0.0%
1.0%
2.0%
3.0%
S O N D J F M A M J J A
2000- 2001 1999- 2000
INSIGHT
Michael F. Easley, Governor Employment Security Commission of North Carolina Thomas Whitaker, Acting Chairman
Volume 2, Number 1 October 2001
North Carolina’s Labor and Economic Outlook
NC Quick Stats: August 2001
Labor Force 3,997,600
Employment 3,796,800
Unemployment 200,800
Unemployment Rate 5.0%
Note: Data are preliminary and are
seasonally adjusted.
INSIDE
Labor Market Abstract ............... 1
Economic Indicators in
North Carolina .............................. 1
Issues in North Carolina’s
Unemployment Insurance System:
Average Duration ......................... 3
Introduction .................................. 3
Economic and Demographic
Determinants of Duration ......... 4
Differences in UI Laws ................ 7
Comparison of North Carolina’s
and Georgia’s Reemployment
Programs ..................................... 7
Conclusion .................................... 9
The Reemployment Outcomes of
Dislocated Manufacturing
Workers ......................................... 11
Methodology: Tracking
Laid- Off Workers ..................... 11
Reemployment After a Layoff .. 13
Wages Earned After a Layoff ... 14
Differences by Age Group ........ 15
Differences by Educational
Level .......................................... 16
Differences by Industry ............ 16
Differences by Race
and Gender................................ 17
Conclusion .................................. 17
( Continued on Page 2)
Labor Market Abstract
During August 2001, the North Carolina seasonally adjusted unemployment
rate decreased to 5.0 percent from 5.3 percent the previous month. During
the same period, the civilian labor force grew by approximately 9,000. Em-ployment
in the service producing industries rose during the month with most
increases occurring in retail trade, hotels & lodging and health services. A
decrease occurred in the manufacturing industry with losses primarily in tex-tiles,
furniture and electronic equipment. Overall, the unemployment level
decreased from an estimated level of 210,800 in July 2001 to 200,800 in Au-gust
2001.
Economic Indicators in North Carolina
Economic indicators used to predict future economic activity are referred to
as leading indicators, while coincident indicators are used to help determine
changes in the economy that are concurrent with such indicators. All graphs
reflect the most recent monthly statewide data.
Insured Unemployment Rates*
Adjusted Unemployment Rates*
Total Nonagricultural Employment, in Thousands*
* Source: ESC, Labor Market Information Division
LMI Happenings ........................ 18
1.0%
2.0%
3.0%
4.0%
5.0%
6.0%
S O N D J F M A M J J A
3,700
3,800
3,900
4,000
4,100
S O N D J F M A M J J A
2
2000- 2001 1999- 2000
Sales and Use Tax Revenues, in Millions
Average Weekly Hours Worked in Manufacturing
Initial Claims
Economic Indicators in North Carolina ( Continued from Page 1)
Percent
Latest Previous Previous Change From
Month Month Year Last Year
Asheville 851 1,529 946 - 10.0
Charlotte 4,027 3,394 2,103 91.5
Durham 1,074 1,126 557 92.8
Fayetteville 1,347 1,661 1,007 33.8
Goldsboro 609 1,144 611 0.0
Greensboro 2,450 2,923 1,834 33.6
Greenville 1,274 1,781 794 60.5
Hickory/ Newton 5,683 10,669 2,099 170.7
Jacksonville 438 365 471 - 7.0
Raleigh 2,834 3,005 1,814 56.2
Wilmington 1,070 1,200 828 29.2
Winston- Salem 2,771 4,837 2,345 18.2
Source: Employment Security Commission
Percent
Latest Previous Previous Change From
Month Month Year Last Year
Asheville 39.8 39.9 41.7 - 4.6
Charlotte/ Gastonia 40.3 39.8 41.7 - 3.4
Greensboro/
Winston- Salem/
High Point 38.9 37.9 40.1 - 3.0
Raleigh/ Durham/
Chapel Hill 41.3 40.3 43.1 - 4.6
Source: Employment Security Commission
Percent
Latest Previous Previous Change From
Month Month Year Last Year
Asheville 211.9 191.3 207.0 2.4
Charlotte 1,024.5 1,124.5 1,062.2 - 3.5
Durham 252.1 253.1 258.4 - 2.4
Fayetteville 197.1 196.9 226.0 - 12.8
Greensboro 521.2 503.4 525.6 -. 01
Greenville 141.9 134.3 143.1 -. 01
Hickory 133.2 125.9 124.1 7.3
Raleigh 617.7 636.0 615.2 0.0
Wilmington 240.4 222.4 224.4 7.1
Winston- Salem 359.6 342.4 369.1 - 2.6
Source: N. C. Department of Revenue, Tax Research Division
Statewide In Selected Cities
In Selected Metropolitan Statistical Areas Statewide
Statewide, in Thousands By ESC Local Offices
( Continued on Page 19)
0
20
40
60
80
100
120
140
160
S O N D J F M A M J J A
37
38
39
40
41
42
43
S O N D J F M A M J J A
$ 0
$ 100
$ 200
$ 300
$ 400
S O N D J F M A M J J A
3
Why is North Carolina’s average du-ration
higher than Georgia’s? This
study looks at this issue from three
perspectives. First of all, there are
economic and demographic differ-ences
in the workforces between the
states. Factors such as the number
of manufacturing workers or the
overall unemployment rate may cause
a given state’s duration to differ from
another state’s, all else equal. The
next section, “ Economic and Demo-graphic
Determinants of Duration,”
attempts to show that some of these
differences imply that North
Carolina’s duration should be higher
than Georgia’s.
Issues in North Carolina’s Unemployment Insurance
System: Average Duration
Introduction
In the Unemployment Insurance ( UI) system, duration refers to the number
of weeks UI claimants receive benefits before returning to work. One of the
primary objectives of the Employment Security Commission ( ESC) is to as-sist
in job search, thereby reducing the average duration of filing for benefits.
A low average duration means UI recipients are returning to work quickly,
thus saving the UI system, and the employers in the state that fund it, consid-erable
sums of money. For example, if North Carolina’s average duration
had been reduced by one week over the last year, the UI Trust Fund would
have saved over $ 64 million. This value is obtained by multiplying the aver-age
weekly benefit amount in the state over the last year ($ 235.31) by the
number of “ first payments” in the same period ( 272,597).
North Carolina would benefit more from a reduction in average duration than
other states in the Southeast because both its average weekly benefit amount
and number of first payments are relatively high. For instance, Georgia and
Virginia, two comparably- sized states, would have saved only $ 44 million and
$ 22 million, respectively, if their durations had been reduced by one week.
Georgia’s average weekly benefit amount was $ 215.39 and its number of
first payments was 203,959 while the corresponding numbers for Virginia
were $ 210.22 and 106,018.
Fortunately, over the last few years North Carolina has had either the 2nd or
3rd lowest duration among the states, after Georgia ( and recently New Hamp-shire).
In the first quarter of 2001, North Carolina’s average duration for the
12- month period was 9.2 weeks, compared to 8.5 weeks for Georgia and 8.7
weeks for New Hampshire. As Figure 1 shows, the average duration in
North Carolina has been consistently above Georgia’s in the last few years. 1
A one- week reduction in North
Carolina’s average duration last
year would have saved its UI Trust
Fund over $ 64 million.
At 9.2 weeks, North Carolina’s
average duration is the third
lowest in the nation.
North Carolina’s low average
duration of filing for UI means
claimants are returning to work
faster than in other states.
7.0
7.5
8.0
8.5
9.0
9.5
10.0
10.5
1998.2 1998.3 1998.4 1999.1 1999.2 1999.3 1999.4 2000.1 2000.2 2000.3 2000.4 2001.1
Quarter
Weeks of Duration
Georgia North Carolina
Figure 1: Average Durations in North Carolina and Georgia
4
Secondly, there are differences in UI laws between North Carolina and Geor-gia.
Although the states must conform to general federal guidelines when
operating a UI system, each state has some flexibility in the procedures of its
UI system. For example, states have different maximum weekly benefit
amounts and they may establish different laws for allowing UI claimants to
refuse job offers. These differences, detailed in the section “ Differences in
UI Laws,” can have important implications for the length of average dura-tions.
Thirdly, reemployment programs in the respective states are likely to have an
important impact on duration. North Carolina and Georgia have both imple-mented
special reemployment initiatives in the last few years. We will
compare these initiatives in the section titled “ Comparisons of North Carolina’s
and Georgia’s Reemployment Programs.”
Economic and Demographic Determinants of Duration
In order to compare North
Carolina’s duration to other
states’, one should look at how
the characteristics of the
economy and workers in the
state affect average duration.
Using just some of the charac-teristics
of the workforce and
other economic factors that are
important to duration, we have
predicted the average duration
for the 50 states. Figure 2 shows
the predicted durations and the
actual durations for the seven
southeastern states. ( See Ap-pendix
1 for a graph of all 50
states.)
North Carolina’s predicted du-ration
is higher than several other southern states, particularly Alabama, South
Carolina, Georgia, Tennessee and Virginia. North Carolina’s actual average
duration was 2.9 weeks shorter than predicted by this model, which was the
largest difference of the states in the region.
The predicted durations were derived using a regression analysis on data
from the 50 states in 2000. With this regression, one can make some general
statements about how certain variables affect duration and how, when taken
together, these variables impact North Carolina’s duration relative to other
states.
Six variables were used to predict average duration. These variables were
chosen based on assumptions that they were important factors in determin-ing
duration, as explained in the following paragraphs. In addition, data on
these variables were readily available for all 50 states.
Differences in UI laws and
reemployment programs in the
respective states may explain the
differences in duration.
Regression analysis on data from
50 states in 2000 predicted North
Carolina’s duration to be higher
than several other southern states,
particularly Alabama, South
Carolina, Georgia, Tennessee and
Virginia.
0
2
4
6
8
10
12
14
16
AL SC GA TN VA NC FL
Weeks
Average Duration Predicted Duration
Figure 2: Average Duration in 2000 Compared with Predicted Duration
5
The first of these variables is the num-ber
of workers in the state who were
covered by unemployment insurance,
i. e. covered employment, in 2000. It
is expected that states with larger cov-ered
employment will have a higher
average duration. Employment of-fices
in larger states may often face
a greater number of job applicants.
North Carolina’s covered employment
is among the largest in the Southeast
region, as shown in Figure 3. How-ever,
it is less than Florida’s and not
significantly higher than either
Georgia’s or Virginia’s.
The second determinant of duration
used is the share of manufacturing
employment in the overall covered em-ployment
in the state. It is expected
that states with more manufacturing
will have higher durations, since many
manufacturing workers have more dif-ficulty
finding reemployment than
workers in other industries. In sup-port
of this, Current Population Survey
( CPS) data show that unemployed
manufacturing workers have longer in-dividual
durations than workers from
other industries. As Figure 4 shows,
among the Southeastern states, North
Carolina has the highest percentage
of its covered workers in the manu-facturing
sector.
The third variable is the maximum
weekly benefit amount for UI recipi-ents
in 2000. It is expected that a
higher maximum benefit amount will
increase duration. More generous
benefits may delay the need for
workers to find reemployment. Fig-ure
5 shows that, among the states in
the Southeast, North Carolina had the
highest maximum weekly benefit
amount as of July 2001. This rose to
$ 396 August 1, 2001.
Figure 3: Covered Employment in 2000
3,823 3,773 3,765
3,230
2,605
1,823 1,784
0
1,000
2,000
3,000
4,000
5,000
FL NC GA VA TN AL SC
In Thousands
Figure 4: Share of Manufacturing
Employment in Overall Employment in 1999
21.6%
20.4% 20.0% 19.7%
16.2%
12.6%
7.4%
0.0%
5.0%
10.0%
15.0%
20.0%
25.0%
NC AL TN SC GA VA FL
$ 331
$ 275 $ 274 $ 259 $ 255
$ 190
$ 375
$ 0
$ 50
$ 100
$ 150
$ 200
$ 250
$ 300
$ 350
$ 400
NC VA FL GA SC TN AL
Dollars per week
Figure 5: Maximum Weekly Benefit Amounts in 2000
6
The average unemployment rate in the
state during fourth quarter 2000 is the
fourth variable. A higher unemployment
rate would imply a weaker labor market,
so that unemployed workers face a
harder time finding employment. This
would lengthen duration in states with high
unemployment. As Figure 6 shows,
North Carolina’s unemployment rate was
high relative to most of the other states
in the region during fourth quarter 2000.
The fifth factor considered important to
duration is whether the state has a one-week
waiting period for UI recipients.
Only twelve states do not have waiting
periods. Two of these states, Georgia
and Alabama, are in the Southeast. These
states may have a higher proportion of short- term unemployed filing for ben-efits
in that first week, which would reduce the state’s average duration.
The final determinant of duration is whether a state is located in the South.
This is used to isolate the idiosyncratic nature of the Southern labor market.
For instance, the lower unionization of the labor force in the South may in-crease
job availability and turnover. Given this, it is likely that duration will be
shorter in Southern states. All of the states in our region are expected to
have lower durations because of this.
The results of the regression are summarized in the following table. All of
the variables had the expected impact on duration, except the share of manu-facturing
in covered employment. The lower duration for states with higher
shares of manufacturing employment may be due to the high proportion of
attached claimants within the manufacturing sector. Attached claimants spend
a short time receiving benefits before returning to work with their company.
North Carolina has a high proportion of attached claims. The impact of
attached claimants cannot be directly obtained because the data on the other
states are unavailable.
Variables affecting duration . . . . . . and their impact estimated by the model
1. covered employment an additional one million workers raised duration by about
two- tenths of a week
2. share of manufacturing in employment a 10 percentage point increase ( from, say, 20% to 30%)
lowered duration by a little more than one week
3. maximum benefit amount a $ 10 increase raised duration by one- tenth of a week
4. average total unemployment rate a 1 percentage point increase raised duration by about
two- thirds of a week
5. 1- week waiting period increased duration by approximately .8 weeks
6. Southern state reduced duration by 1.2 weeks
A one- week waiting period for UI
recipients may affect a state’s
duration rate.
The proportion of attached claims
filed within the manufacturing
sector affects duration.
4.4%
3.8% 3.6% 3.5%
3.2%
2.9%
2.1%
0.0%
1.0%
2.0%
3.0%
4.0%
5.0%
AL TN NC FL GA SC VA
Figure 6: Fourth Quarter 2000 Average Total Unemployment Rates
7
Although the regression explained over half the differences in the actual
durations of the 50 states, it did not predict every state’s duration exactly.
For instance, it overestimated North Carolina’s average duration by nearly
three weeks. There are other factors affecting duration that were not con-sidered
in the model. As mentioned earlier, the number of attached claimants
would be important. A second factor is the reemployment program in the
state, discussed later. Still other factors include the age and racial distribu-tion
of the state’s workforce, as well as, the amount of urbanization within
the state.
Differences in UI Laws
North Carolina and Georgia differ in eligibility requirements and benefits, as
established by their respective UI laws. As previously stated, North Caro-lina
has a higher maximum weekly benefit amount than Georgia, which
contributes to a higher expected duration. North Carolina’s maximum weekly
benefit was $ 375, compared to $ 274 in Georgia. This difference is a result of
the way the two states calculate the maximum weekly benefit: in North
Carolina, it is two- thirds of the average weekly wage in the state while in
Georgia it is less than one- half. Therefore, UI recipients whose high- quarter
earnings are relatively high will receive a larger proportion of their pre- layoff
wages in benefits in North Carolina than in Georgia. For example, a worker
with average weekly earnings of $ 800 would only receive 34% of this in UI
benefits in Georgia, but would have a wage replacement rate of 47% in
North Carolina. Thus, claimants in Georgia have a greater incentive to find
new jobs quickly.
Under the different state laws, it seems that claimants have an easier time
rejecting job offers in North Carolina than in Georgia. Georgia’s law speci-fies
that individuals who receive benefits for 10 or more weeks cannot reject
a job offer if the wages are at least 66 percent of their high- quarter base
period wages. North Carolina does not have such a provision. However, it
is a general practice in North Carolina’s local ESC offices to encourage
claimants who have been unemployed for many weeks to accept jobs which
offer lower wages. Also, North Carolina has a provision in its law that al-lows
individuals to refuse a job if they cannot obtain adequate childcare or
elder care.
Both North Carolina and Georgia determine the duration of benefits based
on wages earned in the base period. Most UI recipients in both states will be
eligible for 26 weeks of benefits. But if workers earned relatively little in the
entire base period compared to the high quarter, then the benefit period may
be reduced. In North Carolina, the minimum benefit period is 13 weeks,
while in Georgia the benefit period may be as low as eight weeks.
Comparison of North Carolina’s and Georgia’s Reemployment
Programs
Again, both North Carolina and Georgia have received state funds in order to
provide a reemployment program for eligible claimants that are receiving
unemployment benefits. The North Carolina program, the Reemployment
Initiative ( REI), was funded in January 2000 and was implemented in April
All factors which may affect
duration were not considered in
the model.
The difference in the way the two
states calculate the maximum
weekly benefit creates a
significant difference in what each
state pays.
There are also differences in
claimant eligibility requirements.
Maximum eligibility in both states
is 26 weeks of benefits.
8
2000. Georgia’s program, the Claimant Assistance Program ( CAP), began
with service to select areas of Georgia in 1987, but expanded to cover the
entire state eighteen months later. Georgia’s CAP was used by North Caro-lina
as a model for its REI; therefore, there are many similarities between the
programs. Some differences exist, as well.
The CAP went through a slow and evolving process over the years to be-come
what it is today. Initially, CAP provided one- on- one contact with
claimants and also offered workshops. Now, almost all efforts are in the
form of one- hour specialty workshops designed to meet the needs of the
claimants based on their input and suggestions. There is, however, one- on-one
time still available for claimants at the workshops. Until recently, CAP
participants included only claimants that were separated from work through
lack of work. Now the program includes those who are without work for
other reasons, as well, such as quitting due to child or elder care or other
cause, being fired, etc. North Carolina’s REI only includes those that have
been separated through lack of work.
The CAP local office staff undergoes training involving six consecutive courses
taught by consultants. Staff members of each district meet every six months
to discuss the program and ways they can improve their performance. There
is also an emphasis on trying to ensure that the most successful staff are
used in the program. Staff members participate in an information exchange
program that matches low performance workers with high performance ones
to improve overall quality. There is consistent monitoring of the performance
of staff members.
One of the reasons Georgia is able to provide so much training for its staff is
that it receives more appropriated funds for its program. Georgia has re-ceived
between $ 14- 19 million per year for the implementation of CAP. This
compares to approximately $ 9 million that was appropriated for the REI pro-gram
in North Carolina during its first year of implementation. CAP is funded
for a five- year time period while REI is funded for two years at a time. Also,
Georgia has received no indication from its legislature that it plans to termi-nate
CAP, while REI funding is not included in North Carolina’s budget
effective July 1, 2001.
Georgia and North Carolina both employ 160-
200 staff members in their programs. However,
Georgia has 53 offices statewide while North
Carolina has around 90 offices. This results in
more staff available at each office in Georgia.
The REI program served approximately 67,000
claimants last year, while CAP served 58,000.
However, the programs offered by the CAP
staff were also provided to 9,000 participants
in Georgia’s UI profiling program.
The CAP is a 17- week long program while REI
is 12 weeks long. Participants in CAP are re-quired
to meet with a staff person after the
first, fifth, ninth and fourteenth weeks of the
Georgia’s reemployment initiative
program was used by North
Carolina as a model for its REI.
In CAP, both methods of providing
claimants services and staff
training have evolved extensively
over time.
Georgia has received between
$ 14- 19 million yearly for CAP
while North Carolina received $ 9
million in the first year for REI.
67,000
58,000
0
10,000
20,000
30,000
40,000
50,000
60,000
70,000
80,000
North Carolina Georgia
Number of Claimants Served by a Reemployment Program in 2000
9
program. Contact is not required after the seventeenth week. REI participants are required to contact staff
either in person, by phone or by e- mail on a weekly basis for the first four weeks and biweekly for the remaining
eight weeks. CAP directs its participants into one of three tracks: self- serve, staff assisted and intensive. In both
programs, participants are subject to adjudication if they do not follow the expectations of the program. However,
this does not happen often.
For the year ending June 2001, Georgia received approximately 252,000 initial separated claims while North
Carolina received around 295,000. Of these, about 23 percent in each state participated in their respective
reemployment program, CAP or REI. These efforts resulted in an entered employment rate of 52.7 percent for
the 17- week CAP and a 44.4 percent rate for the 12- week REI, which in both cases amounts to roughly 30,000
people.
While the main goal of both CAP and REI
is to aid in UI claimants reentering the
workforce as soon as possible, another ben-efit
of both programs is to increase the
savings to each state’s UI Trust Fund. One
way this is obtained is by lowering the du-ration.
Although Georgia’s overall duration
is lower than North Carolina’s, the savings
are somewhat different. It is estimated that
CAP saved Georgia’s trust fund $ 38.9 mil-lion
for one year and North Carolina saved
its trust fund an estimated $ 42 million for
the same time period. Given that roughly
$ 5- 10 million more is spent on Georgia’s
CAP compared to REI, North Carolina’s
REI program is more cost effective.
Both the CAP and REI are beneficial to their participants and trust funds in their respective states. One might
argue that Georgia has been more successful in its reemployment efforts because it has been operating this
program for over a decade and funds it at a higher level than North Carolina. However, because these programs
are so similar and because they affect only a portion of UI claimants in each state, it is unlikely that the differ-ences
between the programs contribute much to the differences between average durations in these two states.
Conclusion
Shortening the length of time UI claimants receive benefits, or average duration, provides significant savings to a
state’s UI system. Currently, North Carolina has the third- lowest average duration among the states, although
Georgia’s duration is .7 weeks shorter. This article has attempted to cover some of the factors that explain why
one state’s duration is not as low as another’s.
Based on the regression analysis of data from the 50 states, North Carolina is expected to have a higher duration
of filing for UI benefits than many of the other states in our geographic area, including Georgia. One of the
factors which tends to push up North Carolina’s duration is its high maximum weekly benefit amount. North
Carolina has the highest maximum weekly benefit of all the states in the Southeast; it is approximately $ 100 more
than Georgia’s. The one- week waiting period for UI recipients is another factor increasing North Carolina’s
duration relative to Georgia’s. Also, North Carolina’s relatively high unemployment rate should make it harder for
UI claimants to find reemployment quickly.
In addition to the variables used in the regression, differences in UI laws between North Carolina and Georgia
would likely imply a higher duration in North Carolina. For instance, North Carolina’s laws permit claimants to
reject a wider range of unfavorable job offers.
$ 39
$ 42
$ 0
$ 5
$ 10
$ 15
$ 20
$ 25
$ 30
$ 35
$ 40
$ 45
North Carolina Georgia
Dollars in Millions
Trust Fund Savings from Reemployment Programs in 2000
10
However, North Carolina’s Employment Service ( ES), particularly its Reem-ployment
Initiative staff, has been very successful in job placement and
employment services. Over the past year, over 770,000 individuals regis-tered
for employment services and 213,005 entered employment after receiving
ES services. During the same period, intensive reemployment assistance
was offered to approximately 67,000 claimants. For these efforts, the aver-age
length of weeks claimants file for UI before obtaining work is currently
9.2 weeks, the third lowest in the nation.
North Carolina’s ESC administrative staff, as well as ES and REI staff, have
one goal in mind: assisting unemployed workers find employment quickly.
An important result of these efforts is a low duration of filing, which provides
savings to the UI Trust Fund and the state’s employers.
1 Average duration is calculated by dividing the weeks compensated in the previous
12 months by the number of first payments in the same period.
North Carolina has the third
lowest average duration for UI
claimants in the nation at 9.2
weeks.
0
2
4
6
8
10
12
14
16
18
AL SC NH MS GA VT AR WI IA SD TN IN VA MI NE DE KY NC MO CT OK ND AZ KS MD
Average Duration Predicted Duration
Appendix 1: Average Duration in 2000 Compared with Predicted Duration for All States
0
2
4
6
8
10
12
14
16
18
ME UT LA ID CO MN FL NV WY TX OR OH RI MT WV NM NJ HI IL PA WA AK CA MA NY
Average Duration Predicted Duration
11
The Reemployment Outcomes of Dislocated
Manufacturing Workers
Recent adverse economic conditions have par-ticularly
impacted the manufacturing sector in
North Carolina. The events of September 11th
will likely contribute to this, as the decline in the
airline and tourist- related industries further re-duces
demand for manufactured products.
Although most of the recent layoffs in manufac-turing
seem to be temporary, there have been
several permanent layoff events in the state in
the last few months. In addition, the volatility in
manufacturing employment may encourage some
workers to voluntarily leave this industry group
in search of a more stable career.
As in most states, the share of manufacturing
employment has been declining in North Caro-lina
for decades. This has led to an absolute drop
in employment in this industry over the last few
years. Between August 1995 and August of this year, employment in manu-facturing
has steadily declined from 862,500 to 731,900. The majority of this
decline has been in the textile industry, where employment has fallen by
approximately 67,000 over this time period. Total employment in the state,
however, continued to rise, with most of the new jobs being reported in the
services and retail trade sectors. Industrial employment projections suggest
that this trend will continue for the near future.
Do dislocated manufacturing workers find jobs with comparable wages?
Wages in the manufacturing sector are somewhat above the state average.
In 1998, the average weekly wage in manufacturing, according to the Em-ployment
and Wages program of the Employment Security Commission ( ESC),
was $ 629.84, 17 percent above the state average wage across all industries.
At the same time, the services industry’s average wage was $ 504.88. Of
course, this is a crude comparison because it does not take into account the
experience or education of the individual workers who make up the employ-ment
in these industries. Such data, as well as anecdotal evidence, do not
give a satisfactorily in- depth answer to how well the dislocated workers do
at maintaining their standards of living.
In this article, we take a different approach by following workers who were
dislocated from their jobs in 1997 and 1998 in order to see how their indi-vidual
wages two years after the layoffs compare to their pre- layoff wages.
This provides a view of a large number of workers during that period, to see
where they actually found a job and how much they actually earned. This
information is pertinent to dislocated workers today.
Methodology: Tracking Laid- Off Workers
The North Carolina ESC has developed a data series which tracks workers
who have lost their jobs due to business closures or permanent layoffs. These
workers, and the companies which laid them off, are identified by the state’s
Manufacturing employment in
North Carolina has been
declining for decades.
In 1998, the average weekly wage
in manufacturing was $ 629.84,
17 percent above the state
average wage across all
industries.
640
680
720
760
800
840
880
1995 1996 1997 1998 1999 2000 2001
In Thousands
Figure 1: Manufacturing Employment in North Carolina
August 1995 - August 2001
12
Mass Layoff Statistics ( MLS) program. This program only identifies busi-nesses
with at least 50 claimants for Unemployment Insurance ( UI) in a
consecutive 5- week period. The data show the employment history and
wage earnings of these workers for four quarters before their separation and
eight quarters afterwards.
The data series answers many questions about laid- off workers, among them:
· In what industries will the laid- off workers find new jobs?
· How much will the laid- off workers earn in their new jobs compared
with their old jobs?
· How long will it take for the laid- off workers to find new jobs?
UI taxes are collected by ESC on a quarterly basis, with no reference to a
person’s starting date within the quarter. Therefore, only quarterly wage
data were given in this series. This presents some limitations because the
hours a given person works during a quarter are not known. For example, if
a worker’s wages are lower in one quarter than another, we cannot deter-mine
if this is caused by a lower hourly wage as opposed to fewer hours of
work. Such information could be obtained by a survey of dislocated work-ers,
but this would be costly and time- consuming.
To calculate the pre- layoff quarterly wage, only the wage data on the four
quarters before the layoff event occurred were used. The maximum quar-terly
wages of the first three quarters of the year were used in this study.
( The 4th quarter [ Oct. – Dec.] was excluded because wages are typically
higher in this quarter due to seasonal reasons, such as end- of- year bonuses
and holiday- related employment. Although earnings in the fourth quarter
should be considered when estimating a worker’s yearly income, we are
only interested in quarterly earnings.)
Post- layoff wages were limited to employers covered by UI in North Caro-lina.
Data could not be tabulated for workers who are self- employed or who
earned wages in a state other than North Carolina.
Table 1 shows the total number of layoffs and manufacturing layoffs for
each quarter during the time period under study — first three quarters of
1997 and 1998. These two years were chosen because they were the most
recent years for which complete data were available. They are somewhat
atypical because the unemployment rate in North Carolina during this period,
and the following two years, was consistently below 4 percent. The majority
of layoffs occurred in the 1st and 2nd quarters of each year.
Table 1: Mass Layoffs by Quarter
Year. Quarter Total Layoffs Manufacturing Layoffs
1997.1 3,077 2,588
1997.2 2,860 1,833
1997.3 1,590 1,436
1998.1 4,158 2,930
1998.2 3,554 2,974
1998.3 1,447 1,110
Lower wages in one quarter may
be a result of fewer hours of work.
Workers involved in this study
were followed four quarters before
layoff and eight quarters after
layoff.
Pre- and post- layoff wage
information were limited to
covered wages in North Carolina.
13
Of the 16,686 workers laid off, 12,871 ( 77 percent) were employed by com-panies
in the manufacturing sector. In some cases, individuals were laid off
more than once during this period. When this occurred, the later observa-tions
for those individuals were deleted. Also, workers who were laid off by
a company in the “ tobacco products” industry were excluded because these
companies tend to have seasonal layoffs. This left 9,405 individuals in the
database. Four industries were responsible for over three- quarters of these
layoffs: textiles, apparel, industrial and commercial machinery, including com-puter
equipment, and furniture.
Reemployment After a Layoff
Approximately 67 percent of the laid- off manufacturing workers found re-employment
within one year and 74 percent were reemployed at the end of
two years. As previously stated, these percentages did not reflect the num-ber
of workers who became self- employed, found jobs outside the state or
retired.
Where did these workers find jobs? The steady decline in the overall num-ber
of manufacturing jobs in the state limits the possibility of reemployment in
these industries. The opportunities available for workers would be deter-mined
by the overall health of the economy and the availability of jobs in the
local area that pay enough to ensure a decent standard of living.
Table 2 shows the industries in which the laid off manufacturing workers
were employed two years after the lay- off event. Many of these workers
who returned to work were able to find jobs in the manufacturing sector. Of
the 44 percent reemployed within the manufacturing industries, 16 percent
were reemployed by their former employer and another 28 percent were
reemployed by a company within the same industry. As expected, a substan-tial
fraction were reemployed in either the services sector ( 24 percent) or the
Prior to analysis, adjustments
were made to the data to account
for anomilies.
Forty- four percent of those
workers who returned to work
found jobs in the manufacturing
industries.
retail trade sector ( 9 percent).
Table 2: Primary Industries of Reemployed Manufacturing Workers Two Years after Separation*
Industry Divisions Percent
Same industry** 19
Manufacturing ( other than same industry) 25
Services 24
Retail Trade 9
Wholesale Trade 4
Construction 3
Government 2
Transportation, Communications and Public Utilities 2
Finance, Insurance and Real Estate 1
Agriculture 1
Industry Code not available 10
* These numbers do not include quarters 1997.1 and 1997.2 due to a large proportion of missing industry codes.
* * Thirty- seven percent of these were reemployed by the same company that laid them off.
14
The reemployment opportunities in the manufacturing sector allowed many workers to find jobs where the skills
they had learned over many years could be used effectively. In the next section, we see that this helps those
workers who find manufacturing jobs retain their relatively high wages. But what happens to the wages of
workers who are unable to return to a manufacturing job?
Wages Earned After a Layoff
The median pre- layoff quarterly wages of
the workers was $ 4,895. One year after
the layoff event, the median wages of the
workers who were reemployed was $ 3,781,
a 23 percent decrease. By the end of two
years, median quarterly wages had risen to
$ 4,329, which was still 12 percent lower
than the wages of this group before the lay-off.
( The median pre- layoff wages of the
workers who were employed two years
after the layoff were slightly higher than
the median wages of all the workers who
were laid off.)
Figure 2 compares the median wages of
workers two years after the layoff to their
pre- layoff wages, based on the industry in which the workers were reemployed. It is important to note that
reemployment occurred mainly in the manufacturing, services and retail trade sectors. ( Refer to Table 2.)
The median wage of the workers who were reemployed in manufacturing was approximately the same as the
median wage of this group before the layoff. Those reemployed by the same company or in the same sub-industry
tended to do slightly better than those who found employment in a different manufacturing industry.
Those who found jobs in the services and retail trade industries faced significant reductions in their quarterly
wages. The median wage for the workers reemployed in the retail trade industry was only 63 percent of the pre-layoff
median wage for this group. It should be noted that the workers who went into the services or retail trade
sectors had lower pre- layoff median wages than the other workers. This may suggest that they had, on average,
lower skills or less tenure than other workers.
Another way of looking at the change in
wages before the layoff and afterwards
is to look at the distribution of each
individual’s change in wages. This
analysis is summarized in Figure 3. Two
years after the layoff event, 53 percent
of the workers who were working earned
less than 90 percent of their pre- layoff
wages; 18 percent earned within 10 per-cent
of their pre- layoff wages; and 29
percent earned more than 110 percent
of their pre- layoff wages. Furthermore,
20 percent earned less than half their
pre- layoff wages and 2 percent earned
more than twice as much.
87%
88%
87%
94%
$ 0
$ 1,000
$ 2,000
$ 3,000
$ 4,000
$ 5,000
$ 6,000
Less than 9 years 9 - 11 years 12 years 13 - 16 years
Median Pre- Layoff Wages Median Wages Two Years after Layoff
Figure 2: Quarterly Median wages of Reemployed Manufacturing
Workers Two Years After Layoff
53%
18%
29%
Earned Less Than 90%
Earned Within 10%
Earned More Than 110%
Figure 3: Wages Earned Two Years After Layoff as a Percentage of
Pre- Layoff Wages
15
One may assume that workers with higher wages before the layoff did better than lower- income workers.
Presumably, workers with higher incomes have more job skills, gained through a combination of education and
on- the- job training. Many of these skills can be transferred into new careers, allowing the workers to continue
earning relatively high wages. However, some of the skills may not be transferable. The returns to these job-specific
skills could be lost when workers move into other occupations.
As previously stated, workers with
higher quarterly wages before a lay-off
earned higher wages two years
afterwards than workers with lower
pre- layoff wages. However, Figure 4
shows that the higher- paid workers did
not do as well at replacing their wages.
Workers in the highest income group
had the lowest wage replacement rate
( 58 percent).
Also, only 67 percent of the workers
in the highest wage category were re-employed
by a company in the state
two years after the layoff, which is sig-nificantly
lower than the reemployment
rates of the other groups. It is likely
that a larger fraction of these workers
sought employment outside the state or became self- employed. However, as noted earlier, the data do not
contain information on the wages of these workers.
Differences by Age Group
One might expect that workers in different age brackets would have different experiences after a layoff. Young
workers were more likely to do well after being laid off than older workers because they had put less of an
investment into their jobs and had a greater incentive to move to a different job in order to develop new skills.
Older workers had invested a lot of their time developing skills which were specific to their jobs and, therefore,
would likely find it harder to get new jobs outside their field that paid as well.
Figure 5 shows the median pre- layoff
wages of workers compared to their
wages two years after the layoff event,
where workers were separated into age
groups. The workers under the age of
30 earned 90 percent of their pre- layoff
wage, while workers in the 55 and over
age group earned only 78 percent of their
pre- layoff wage. The median wage in
the middle two age groups was approxi-mately
87 percent of the pre- layoff
median wage. Older workers were also
less likely to re- enter the job market.
Only 52 percent of the workers aged 55
and over had returned to work after two
years, compared to approximately 77
percent of the workers in the other age
groups.
58%
73%
86%
93%
117%
$ 0
$ 2,000
$ 4,000
$ 6,000
$ 8,000
$ 10,000
$ 12,000
$ 0-
$ 2,999
$ 3,000 -
$ 4,999
$ 5,000 -
$ 6,999
$ 7,000 -
$ 9,999
$ 10,000 -
$ 14,999
Pre- Layoff Wages
Median Pre- Layoff Wage Median Wage Two Years after Layoff
Figure 4: Quarterly Median Wages of Manufacturing Workers by Pre- Layoff
Wage Categories
90% 87% 87%
78%
$ 0
$ 1,000
$ 2,000
$ 3,000
$ 4,000
$ 5,000
$ 6,000
Under Age 30 Age 30 to 44 Age 45 to 54 Age 55 and Over
Median Pre- Layoff Wage Median Wages Two Years after Layoff
Figure 5: Median Wages Two Years After Layoff Compared to Pre- Layoff
Wages - by Age Group
16
Differences by Educational Level
Education is an important indicator of earnings. More educated workers typically have greater opportunities for
employment and earn higher wages. Figure 6 compares the pre- layoff wages to wages earned two years after
the layoff by educational level. Both before and after the layoff, median wages increased as the level of educa-tion
rose. However, workers in manufacturing who have more years of education are challenged to find jobs
which allow them to sustain their relatively higher standard of living; many of them may possess firm- specific
skills which will not be compen-sated
by a different employer.
The group of workers with 12 years
of education made up 55 percent
of the sample. The median wage
for this group two years after the
layoff was 88 percent of the pre-layoff
wage. The median wage of
the workers with less than nine
years of education was 94 percent
of the pre- layoff median wage for
this group. However, only 58 per-cent
of this group were reemployed
two years after their layoff. This
might suggest that many of the large
number of older workers in this
group withdrew from the labor mar-ket
to retire or return to school for
further training.
Differences by Industry
As stated earlier, there were
four manufacturing industries
which contributed the majority
of the layoffs in our database:
textiles, apparel, industrial and
commercial machinery, includ-ing
computer equipment, and
furniture. These, notably, are
also the industries that seem to
have been particularly hard hit
during the current economic
downturn. Figure 7 shows the
median wages in each of these
industries before a layoff and
two years afterwards for laid-off
workers who found
reemployment in the state.
Median wages were highest in
the textile and industrial machin-ery
industries before the layoff,
but workers in the furniture in-dustry
did relatively better after
the layoff.
87%
88%
87%
94%
$ 0
$ 1,000
$ 2,000
$ 3,000
$ 4,000
$ 5,000
$ 6,000
Less than 9 years 9 - 11 years 12 years 13 - 16 years
Median Pre- Layoff Wages Median Wages Two Years after Layoff
Figure 6: Median Wages Two Years After Layoff Compared to Pre- Layoff
Wages - by Educational Levels
94%
106%
88%
84%
$ 0
$ 1,000
$ 2,000
$ 3,000
$ 4,000
$ 5,000
$ 6,000
Textiles Apparel Furniture Industrial and
Commercial
Machinery
Median Pre- Layoff Wages Median Wages Two Years after Layoff
Figure 7: Median Wages Two Years After Layoff Compared to Pre- Layoff
Wages - by Industry
17
Workers in the furniture industry were much more likely to find reemployment with other companies within the
same industry than workers in the other industries. This may be due to the geographic concentration of the
companies that manufacture furniture. Workers in the industrial machinery industry were most likely to be
reemployed in the services sector of the economy. However, most of these service jobs were in the “ help supply
services,” which includes many high- tech contract workers.
Differences by Race and Gender
Figure 8 shows the median wages
of the workers who were laid off
by gender. There are significant dif-ferences
between the median wages
of men and women before the lay-offs.
Much of this difference was
attributable to the fact that a larger
percentage of the women were em-ployed
in the low- paying apparel
industry ( 20 percent, compared to 8
percent of men). After two years,
the median wage of the women was
85 percent of the median pre- layoff
wage, while the comparable figure
for men was 89 percent. The re-employment
rates between men and
women were approximately equal.
As illustrated in Figure 9, there was not a significant difference in the median pre- layoff wage of workers among
the racial groups. The median wage for whites two years after the layoff was 86 percent of the pre- layoff
median wage for this group, compared to 88 percent for blacks and 92 percent for the other racial groups
combined ( Native Americans, Asians and Hispanics). The only significant difference was in the reemployment
rates: approximately 78 percent of blacks had returned to a job within the state, compared to 71 percent of
whites.
Conclusion
Recent dislocations in the manufac-turing
sector of the North Carolina
economy have led to great concern
about the future living standards of
those workers who have been laid
off. This study has looked at manu-facturing
workers who were laid off
in either 1997 or 1998, and who filed
for UI benefits, to identify their re-employment
experiences related to
where they achieved reemployment
and how much they earned as a per-centage
of their earnings before the
layoff ( that is, their wage replace-ment
rates).
85%
89%
$ 0
$ 1,000
$ 2,000
$ 3,000
$ 4,000
$ 5,000
$ 6,000
Men Women
Median Pre- Layoff Wage Median Wage Two Years after Layoff
Figure 8: A Comparison of Median Pre- Layoff Wage to Median Wage Two Years
After Layoff - by Gender
92%
88%
86%
$ 0
$ 1,000
$ 2,000
$ 3,000
$ 4,000
$ 5,000
$ 6,000
White Black Other
Median Pre- Layoff Wages Median Wages Two Years after Layoff
Figure 9: Median Wages Two Years After Layoff Compared to Pre- Layoff
Wages - by Race
18
Overall, 74 percent of the dislocated manufacturing workers were reem-ployed
in the state after two years. The median wage of these workers was
88 percent of the median pre- layoff wage. The workers who were reem-ployed
in the manufacturing sector were able to retain their pre- layoff
standards of living, based on the median wages of this group. The workers
who fared the worst were reemployed in either the services or retail trade
industries. Unfortunately, a large fraction of workers ( at least one- third) fell
into these categories.
This study also compared the wage replacement rates of workers in differ-ent
demographic groups. Among the findings: ( 1) lower- paid workers had
higher replacement rates than high- paid workers, ( 2) workers under the age
of 30 did better than older workers, ( 3) workers with less than nine years of
education had the highest replacement rate, ( 4) men did slightly better than
women, and ( 5) whites did slightly less well than blacks or other races. These
results are based on median wages; individual outcomes may vary.
The results of this study indicate that in an economy where workers are
expected to move from the manufacturing sector to service- related indus-tries,
more must be done to ensure that the workers’ standards of living are
maintained. Among the measures that might be appropriate are skill training
in service industry occupations, educational advancements and specialized
job placement services.
Those reemployed within the
manufacturing sector fared better
than those reemployed within the
services or retail trade industries.
In moving from manufacturing to
service industry employment,
workers may need specialized
assistance.
Internet Usage in Filing Claims
Offering those laid off from their jobs the ability to file initial
claims via the Internet is an example of ESC's commitment to
the convenience of its customers. Internet filing of initial claims
was first offered to claimants in October 2000. During that pe-riod,
43 initial claims were submitted. This has grown to 2,512 in
August 2001.
LMI Happenings: New Research and Products from the LMI Division
Changes in Occupational Employment
Statistics
In an effort to provide more timely occupational
employment by industry and occupational wage
information, the Occupational Employment
Statistical ( OES) survey will shift from being
annual to biannual starting with the 2001
survey. North Carolina is one of five states
that volunteered to test the two panel concept.
Those normally surveyed annually, 13,000
employers representing 1.1 million employed,
will be divided equally between the two panels.
Each panel will still include the basic
components and requirements of a yearly
survey. The Bureau of Labor Statistics, who
draws North Carolina’s sample and oversees
its management, will continue to receive
monthly reports as to the progress. The North
Carolina OES staff’s mission as a volunteer
state is to document, particularly logistically,
any problems and to troubleshoot any
unforeseen occurrences. Being a test state
enables North Carolina to “ wet its feet” before
the rest of the nation undertakes the new two
panel format.
0
500
1,000
1,500
2,000
2,500
3,000
Oct.
2000
Nov.
2000
Dec.
2000
Jan.
2001
Feb.
2001
Mar.
2001
Apr.
2001
May
2001
Jun.
2001
Jul.
2001
Aug.
2001
Submitted Internet Initial Claims
19
2000- 2001 1999- 2000
New Vehicle Registrations Housing Units Authorized by Building Permits
Unadjusted Construction Employment, in Thousands New Business Incorporations
Personal Income Tax Revenues, in Millions
Economic Indicators in North Carolina ( Continued from Page 2)
Total Tax Revenues, in Millions
Source: US Census Bureau
Source: NC Department of Revenue
Source: NC Secretary of State, Corporations Division
Source: NC Automobile Dealers Association
Source: ESC, Labor Market Information Division
Source: NC Department of Revenue
0
10,000
20,000
30,000
40,000
50,000
60,000
S O N D J F M A M J J A
0
2,000
4,000
6,000
8,000
10,000
S O N D J F M A M J J A
200
210
220
230
240
250
260
S O N D J F M A M J J A
0
1,000
2,000
3,000
4,000
5,000
6,000
S O N D J F M A M J J A
$ 0
$ 500
$ 1,000
$ 1,500
$ 2,000
S O N D J F M A M J J A
$ 0
$ 200
$ 400
$ 600
$ 800
$ 1,000
$ 1,200
$ 1,400
S O N D J F M A M J J A
20
750 copies of this publication were
produced at a cost of $ 225.00 or
$ 0.30 per copy.
EMPLOYMENT SECURITY COMMISSION OF NORTH CAROLINA
www. ncesc. com
NCESC 6059 October 2001
LABOR MARKET INFORMATION DIVISION
EMPLOYMENT SECURITY COMMISSION OF NORTH CAROLINA
POST OFFICE BOX 25903
RALEIGH, NORTH CAROLINA 27611- 5903
OFFICIAL BUSINESS
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